CN101943916A - Kalman filter prediction-based robot obstacle avoidance method - Google Patents
Kalman filter prediction-based robot obstacle avoidance method Download PDFInfo
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Abstract
The invention relates to a Kalman filter prediction-based robot obstacle avoidance method. In a complex environment, the robot travelling environment changes dynamically; and when an environment of a preplanned mission is determined to have a significant change, the target objective is modified, the target is planned in real time, and the path is modified. In the obstacle avoidance method, a path scheduler sorts a target set according to a digital map, the target set and the state of the robot acquired by a sensor system so as to generate a robot travelling point sequence; the robot travelling point sequence is executed by a servo system; when the sensor system detects a new obstacle, a Kalman filter model is established according to observation data; and parameters are identified and modified by using the observation data and an expectation maximization model identification algorithm of a classical linear dynamic system; and the digital map is updated for the next turn of local re-planning of the path scheduler. The method can realize obstacle avoidance path planning of the robot generated locally and dynamically in an undetermined environment, and has the advantages of simple implementation and good real-time performance.
Description
Technical field
The present invention relates to a kind of robot obstacle-avoiding method based on the Kalman filter prediction.
Background technology
Continuous development along with artificial intelligence, electronic communication technology, robot is except entering military fields such as unmanned vehicle and unmanned underwater robot, also progress into weather forecast, the scouting of lifesaving task, the many domestic lifes of environmental monitoring fields such as search and forest prairie fire.In the applied environment of complexity, it is dynamic change and uncertain that robot advances, therefore, when it carried out a plan target, in case determine the environment generation great change of preplanned mission, it should revise task object, and real-time again planning tasks, with the variation that conforms.Keeping away barrier is one of difficult point among the robot path planning, and its task is in having the environment of barrier, according to certain evaluation criterion, seeks a collisionless path that arrives dbjective state from initial state.Can in complex environment, finish the important embodiment that local online collision prevention is robot autonomous property with static and dynamic barrier.
Robot path planning method commonly used can be divided into global path planning method and the unknown fully or partly unknown local paths planning method of environmental information that environmental information is known fully.Voronoi figure method is expressed the environmental constraints condition, is a kind of global path planning method commonly used, effective.Yet robot is for environmental information, particularly the information of dynamic barrier often is difficult to have priori, the mobile robot can only carry out the map building in the static environment simultaneously, can not in complex environment, finish online collision prevention, the use of this global path planning method of Voronoi figure method is restricted.
Summary of the invention
The purpose of this invention is to provide a kind of robot obstacle-avoiding method, ensure that robot implements to keep away barrier in real time in complexity, uncertain environment, finish set task until robot based on the Kalman filter prediction.
The technical solution adopted in the present invention is:
A kind of robot obstacle-avoiding method based on the Kalman filter prediction is characterized in that, comprises following steps:
Step 1: set the numerical map of initial robot task environment, and be sent to robot path planning's device;
Step 2: sensing system is to the measuring-signal of robot state of living in input signal as robot path planning's device;
Step 3: the path planner of robot is according to numerical map, robot state of living in, task-set and the constraint condition grasped at present, task-set is sorted, cook up the collisionless path of advancing, and produce the progress point sequence signal of a series of robots servo-drive system;
Step 4:, adjust sensing system corresponding real time data is monitored and gathered to environment and robot displacement state based on the output signal of servo-drive system;
Step 5: path planner is carried out state and is judged according to the feedback of sensing system; If the displacement state with last time path planner impact point position coordinates identical, planning end; Otherwise, carry out the content of step 6;
Step 6: judge to have or not new barrier to occur,, continue the action of advancing of servo-drive system in the execution in step four if no new barrier occurs; Otherwise, carry out the content of step 7;
Step 7: set up new barrier kalman filter models,, model parameter is carried out identification and correction according to the observation data of sensing system;
Step 8: the prediction that concerns between model, robot location and the surrounding environment according to identification, whether inspection machine people advances can bump in the process, as does not predict and can bump, and continues the action of advancing of servo-drive system in the execution in step four; Otherwise, carry out the content of step 1, upgrade numerical map, carry out the local weight-normality in path of a new round for path planner and draw.
In the step 3, when robot sorts to task-set, initial position according to known given robot starting point, impact point and the known barrier of part, set up Voronoi figure state space, according to dijkstra's algorithm or DoubleSweep algorithm, can search out a collisionless path along Voronoi figure summit, and produce a series of robots servo-drive system progress point sequence signal from the robot starting point to impact point.
In the step 7, observation data according to sensing system 2, when the kalman filter models parameter of new barrier is carried out identification and correction, adopt linear dynamic system expectation maximization (EM) Model Distinguish algorithm, characterize as follows with dynamic system equation and measurement equation goal systems:
Wherein:
tMoment state variable
, observational variable
, system noise
, measure noise
A Be the dynamic model transport function,
C It is the measurement model transport function;
Q With
R Be respectively system noise variance and measurement noise variance; Can calculate model and parameters such as A, C, Q and R with existing linear dynamic system expectation maximization (EM) Model Distinguish algorithm iteration according to observation data, finish Model Distinguish.
Described Kalman filter model is that whole filtering comprises forecasting process and renewal process by the optimal estimation of measuring the resulting observation information solving system of equation state; Described forecasting process is responsible for calculating current state variable and error covariance estimated value, so that be the prior estimate of next time state structure; Described renewal process is responsible for feedback, and it estimates that to construct improved posteriority its prediction and renewal equation characterize as follows with prior estimate and new measurand combination:
The single step prediction:
Single step is upgraded:
Wherein,
It is mathematical expectation
,
It is the covariance expectation
In order to make Kalman filter start working, need to set two zero initial values constantly of Kalman's model:
With
(empirical value is often got
,
);
Kalman filter model adopts the recursion calculation mode, needs only the given initial shape value of estimating
With estimate variance
, in conjunction with
tObserved reading constantly (t=l, 2 ...), just can recursion, iterative computation obtains convergent gradually
tState estimation constantly
Suppose the time interval at limited weak point
In, goal systems is gradual, promptly systematic parameter approximate constant (or changing less) then utilizes dynamic system equation and measurement equation iteration to extrapolate
The status predication estimated value of moment internal object is as follows:
More than characterize for equation.
The present invention has the following advantages:
Robot obstacle-avoiding method involved in the present invention has been added the prediction to the relation between robot location and the surrounding environment on Voronoi figure method basis, generation is to the priori of the prediction of the maneuvering target in the surrounding environment along certain direction forward information, thereby make robot can in uncertain environment, dynamically generate the part keep away the barrier path, realize that simply real-time is better.
Description of drawings
Fig. 1 is the robot obstacle-avoiding method schematic diagram based on the Kalman filter prediction.
Fig. 2 is robot initial path and maneuvering target kalman filter models status predication trajectory diagram.
Fig. 3 draws path profile for the local weight-normality of the robot obstacle-avoiding that carries out according to maneuvering target kalman filter models status predication.
Embodiment
In order in complex environment, to realize keeping away effectively barrier, must wait the information data of collecting surrounding enviroment by sensor, it is imperative to carry out its location status precognition effectively according to the characteristics of motion of motor-driven barrier, and the core of path planning method is to determine barrier state in the impact point and the zone of executing the task in the dynamic environment.Barrier-avoiding method of the present invention has carried out perfect to traditional Voronoi figure method, utilization is to the motion observation of motor-driven barrier, kalman filter models according to identification is effectively predicted the characteristics of motion of motor-driven barrier, in case environment generation great change, task object can be revised by robot, and real-time planning tasks again, with the variation that conforms, draw through local repeatedly weight-normality and to obtain the feasible secure path of robot and to implement to keep away the barrier strategy.
The present invention adopts the closed loop framework of " perception-planning-execution ", proposes a kind of robot obstacle-avoiding method based on the Kalman filter prediction.The path planner 3 of robot is according to numerical map 1, task-set, sensing system 2 gained robots self state of living in and the constraint condition grasped at present, task-set is sorted, and produce a series of robots progress point sequence, carry out by controller Control Servo System 4.When having detected new barrier, robot sensor system 2 occurs, set up kalman filter models according to observation data, and utilize observation data and classical linear dynamic system expectation maximization (EM) Model Distinguish algorithm that model parameter is carried out identification and correction.According to Kalman filter model identification 5, the relation between robot location and the surrounding environment is carried out status predication, whether inspection machine people advances can bump in the process.As predict and can bump, just robot carries out the local weight-normality of a new round and draws in time with the updating location information numerical map 1 to barrier in the surrounding environment that predicts for path planner 3.The optimizing process of " perception-planning-execution " goes round and begins again, and finishes set task until robot.
The obstacle avoidance system that barrier-avoiding method of the present invention is related includes sensing system, path planner, servo-drive system.Wherein, sensing system 2 is collected the information data of surrounding enviroment, carries out the precognition of its location status effectively according to the characteristics of motion of motor-driven barrier; Robot self state of living in and constraint condition that path planner 3 obtains according to numerical map, task-set, sensing system, task-set is sorted, and produce the point sequence that a series of robots advance, cook up the optimum travel track of robot from starting point to impact point
Servo-drive system 4 is carried out the point sequence of being advanced by the robot of path planner generation.
Concrete implementation step of the present invention is as follows:
Step 1: set the numerical map 1 of initial robot task environment, and be sent to robot path planning's device 3.
Step 2: the measuring-signal of 2 pairs of robot states of living in of sensing system is as the input signal of robot path planning's device 3, comprises the relative position of robot and speed etc.
Step 3: the path planner 3 of robot is according to numerical map 1, robot state of living in, task-set and the constraint condition grasped at present, task-set is sorted, promptly, set up Voronoi figure state space according to the initial position of known given robot starting point, impact point and the known barrier of part.Want avoidant disorder, finish preplanned mission safely, need take all factors into consideration the restriction of robot navigation's precision and maneuverability, the optimum from the robot starting point to impact point or the travel track of suboptimum.According to dijkstra's algorithm or DoubleSweep algorithm, can search out a collisionless path along Voronoi figure summit, and produce a series of robots servo-drive system 4 progress point sequence signals from the robot starting point to impact point.
Step 4: servo-drive system 4 is by the progress point sequence of the upgrading action of advancing, and monitors and gather corresponding real time data based on output signal adjustment 2 pairs of maneuvering targets of sensing system of servo-drive system 4 or barrier, displacement state.
Step 5: path planner 3 is carried out state and is judged according to interior ring backfeed loop.If the position coordinates of displacement state with last time the path planner target location coordinate identical, show the arrival target, the planning end; Otherwise, carry out the content of step 6.
Step 6: judge to have or not new barrier to occur: if no new barrier occurs, the action of advancing of continuation execution in step four servo-drive systems 4; Otherwise, carry out the content of step 7.
Step 7: set up new barrier kalman filter models,, and utilize observation data and existing linear dynamic system expectation maximization (EM) Model Distinguish algorithm that model parameter is carried out identification and correction according to the observation data of sensing system 2.Characterize as follows to goal systems with dynamic system equation and measurement equation:
Wherein:
tMoment state variable
, observational variable
, system noise
, measure noise
A Be the dynamic model transport function,
C It is the measurement model transport function;
Q With
R Be respectively system noise variance and measurement noise variance;
Can calculate model and parameters such as A, C, Q and R, the i.e. identification of model with existing linear dynamic system expectation maximization (EM) Model Distinguish algorithm iteration according to observation data.
Step 8: according to the model of identification, to the prediction that concerns between robot location and the surrounding environment, whether inspection machine people advances can bump in the process.As do not predict and can bump, continue the action of advancing of servo-drive system 4 in the execution in step four; Otherwise, carry out the step 1 content, upgrade numerical map 1, carry out the local weight-normality in path of a new round for path planner 3 and draw.
By the optimal estimation of measuring the resulting observation information solving system of equation state, whole filtering comprises forecasting process and renewal process to the Kalman filtering problem often.Forecasting process is responsible for calculating current state variable and error covariance estimated value, so that be the prior estimate of next time state structure; Renewal process is responsible for feedback, and it is estimated prior estimate and new measurand value combination to construct improved posteriority.
The single step prediction:
Single step is upgraded:
Wherein,
It is mathematical expectation
,
It is the covariance expectation
In order to make Kalman filter start working, need to set two zero initial values constantly of Kalman's model:
With
(empirical value is often got
,
).
Kalman filtering algorithm adopts the recursion calculation mode, needs only the given initial shape value of estimating
With estimate variance
, in conjunction with
tObserved reading constantly (t=l, 2 ...), just can recursion, iterative computation obtains convergent gradually
tState estimation constantly
Suppose the time interval at limited weak point
In, goal systems is gradual, promptly systematic parameter approximate constant (or changing less) then utilizes dynamic system equation and measurement equation iteration to extrapolate
The status predication estimated value of moment internal object.
More than characterize for equation.
Embodiment:
In robot initial path shown in Figure 2 and the maneuvering target kalman filter models status predication trajectory diagram, robot and maneuvering target are at the two-dimensional level in-plane moving of 90m * 70m scope.Wherein:
Robot initial position: Start;
Dynamic object point: target.
Robot will arrive at a scouting point B earlier and finish certain task before converging with target;
Avoid the set of obstacle, the position (Z of each obstacle
x(i), Z
y(i)), i=1,2,--, and N}, N gets 12 in the example;
After robot arrived at task point B, its airborne sensor had just been found a new middle low-speed maneuver target;
Robot mobility is more superior than other maneuvering target;
It is the shortest that the constraint condition of robot course planning is that the prerequisite avoiding bumping is issued to the object time;
Robot can be dependent on data chainning communication to the motor-driven information updating of distant object point target;
The time interval h of dbjective state prediction is 2s.
By robot obstacle-avoiding method based on Kalman filter prediction ambient condition, sequential operation according to the following steps:
According to given starting condition, the Voronoi of foundation figure shown in elongated dotted line among Fig. 2, has promptly generated robot and barrier (black circle) state space.Adopt dijkstra's algorithm to obtain the robot initial path planning, shown in thick black dotted line among Fig. 2.
Owing to after robot arrives at a B, find to have new middle low-speed maneuver obstacle Th.By to the observation of the 12s movement position of Th, utilize above-mentionedly based on kalman filter models status predication method, pick out approximate model parameter with the EM algorithm
A * ,
C * , Q * With
R *
The predicted time interval steps is got 0.1s, obtains maneuvering target Th and target prediction locus, i.e. shown in Fig. 2 culminant star line.
According to kalman filter models status predication paths planning method, robot finds if after the original initial planning circuit of continuation marched to E point (2s), robot can bump with motor-driven obstacle Th.Therefore, before robot marches to the E point,, need predicted position D point, implement the local path weight-normality and draw in conjunction with obstacle location of predicting behind the 2s and dynamic object point target according to above-mentioned prediction obstacle-avoiding route planning.The robot path that thick black dotted line among Fig. 3 is attached most importance to and planned.
As shown in Figure 3, should can effectively avoid bumping based on the robot obstacle-avoiding method of kalman filter models status predication with barrier, this method has ensured that robot implements to keep away barrier in real time in complexity, uncertain environment, finish set task until robot.
Claims (4)
1. the robot obstacle-avoiding method based on the Kalman filter prediction is characterized in that, comprises following steps:
Step 1: set the numerical map (1) of initial robot task environment, and be sent to robot path planning's device (3);
Step 2: sensing system (2) is to the measuring-signal of robot state of living in input signal as robot path planning's device (3);
Step 3: the path planner of robot (3) is according to numerical map (1), robot state of living in, task-set and the constraint condition grasped at present, task-set is sorted, cook up the collisionless path of advancing, and produce the progress point sequence signal of a series of robots servo-drive systems (4);
Step 4:, adjust sensing system (2) corresponding real time data is monitored and gathered to environment and displacement state based on the output signal of servo-drive system (4);
Step 5: path planner (3) is carried out state and is judged according to the feedback of sensing system (2); If the displacement state with last time the path planner target location coordinate identical, planning end; Otherwise, carry out the content of step 6;
Step 6: judge to have or not new barrier to occur:, continue the action of advancing of servo-drive system (4) in the execution in step four if no new barrier occurs; Otherwise, carry out the content of step 7;
Step 7: set up new barrier kalman filter models,, model parameter is carried out identification and correction according to the observation data of sensing system (2);
Step 8: the prediction that concerns between model, robot location and the surrounding environment according to identification, whether inspection machine people advances can bump in the process, as does not predict and can bump, and continues the action of advancing of servo-drive system (4) in the execution in step four; Otherwise, carry out the content of step 1, upgrade numerical map (1), carry out the local weight-normality in path of a new round for path planner (3) and draw.
2. a kind of robot obstacle-avoiding method according to claim 1 based on the Kalman filter prediction, it is characterized in that: in the step 3, when robot sorts to task-set, initial position according to known given robot starting point, impact point and the known barrier of part, set up Voronoi figure state space, according to dijkstra's algorithm or DoubleSweep algorithm, search out a collisionless path along Voronoi figure summit, and produce a series of robots servo-drive systems (4) progress point sequence signal from the robot starting point to impact point.
3. a kind of robot obstacle-avoiding method according to claim 1 based on the Kalman filter prediction, it is characterized in that: in the step 7, observation data according to sensing system (2), when the kalman filter models parameter of new barrier is carried out identification and correction, adopt linear dynamic system expectation maximization Model Distinguish algorithm, characterize as follows with dynamic system equation and measurement equation goal systems:
Wherein:
tMoment state variable
, observational variable
, system noise
, measure noise
A Be the dynamic model transport function,
C It is the measurement model transport function;
Q With
R Be respectively system noise variance and measurement noise variance; Calculate model and parameters such as A, C, Q and R according to observation data with existing linear dynamic system expectation maximization Model Distinguish algorithm iteration, finish Model Distinguish.
4. a kind of robot obstacle-avoiding method according to claim 1 based on the Kalman filter prediction, it is characterized in that: described Kalman filter model, be that whole filtering comprises forecasting process and renewal process by the optimal estimation of measuring the resulting observation information solving system of equation state; Described forecasting process is responsible for calculating current state variable and error covariance estimated value, so that be the prior estimate of next time state structure; Described renewal process is responsible for feedback, and it estimates that to construct improved posteriority its prediction and renewal equation characterize as follows with prior estimate and new measurand value combination:
The single step prediction:
Single step is upgraded:
Wherein,
It is mathematical expectation
,
It is the covariance expectation
In order to make Kalman filter start working, need to set two zero initial values constantly of Kalman's model:
With
, empirical value is often got
,
Kalman filter model adopts the recursion calculation mode, needs only the given initial shape value of estimating
With estimate variance
, in conjunction with
tObserved reading constantly, recursion, iterative computation obtain convergent gradually
tState estimation constantly
, t=1,2
Suppose the time interval at limited weak point
In, goal systems is gradual, promptly systematic parameter is approximate constant, then utilizes dynamic system equation and measurement equation iteration to extrapolate
The status predication estimated value of moment internal object is as follows:
More than specifically characterize for equation.
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